Running lots of standards in one run, best Fit Type

I'm running about 15-20 standards in a run, injected after every 6 samples, and I want to change the set up to modified standard throughout so inject a standard every 6 samples, add that point to the calibration curve and at the end ill have up to 20 standard points in the cal curve and use this curve to quantify ALL the previous samples. Is this more accurate than running sliding with/without overlap? I would have though using all the standards would be more accurate but another argument is that retention time and viscosity shifts may require the overlap method. What fit type is best for this- Linear thru Zero?

Also, whats the difference between Average by Amount and Average by Level? If you run 20 standards that all have the same concentration (eg 10.007 mcg/mL) and you put this in the Value of your component editor surely average by amount will average ALL these standards, same as if you set a Level of 1 next to these standards also?


Thanks for reading.

Best Answer

  • MJSMJS
    Accepted Answer
    I think that DavidHPLC's question about why you have so many is key to answering your questions.
    "Is this more accurate than running sliding with/without overlap?"
    -Maybe, but I think it all depends on what you observe with regard to system drift over the course of the analysis.  Does it really make sense for your analysis to quantify the first few sample injections with data from a standard injection that was many hours later in the run?  I would imagine that this would make it less accurate than a sliding overlap calibration, but this could be a simple exercise if you really want to know for sure.  Inject your standard as the sample as well and see how it drifts over the sample set when you have your 15-20 standard length run.  How does the accuracy change when using either of the calibration approaches?  What sort of %RSD do you have for the different brackets vs overall?  Once you have the data, you then have an informed decision on how to best calibrate for your specific method. 

    "I would have though using all the standards would be more accurate but another argument is that retention time and viscosity shifts may require the overlap method."
    -What observations of retention time or viscosity shifts have you observed? 

    "What fit type is best for this- Linear thru Zero?"
    -If you only have a single concentration std and not a 5+point linearity, then linear, linear through zero fit types are the same whether you choose it or not.  

    Average by amount/level
    -you are right when using a single calibration point, they are all the same.  The only thing is that average by level is only useful when you've configured the quantification by level in the sample set if I recall correctly, just like average by amount is really only useful if you use varied amounts.

Answers

  • STD1
    SMP1-6
    STD2
    SMP7-12
    STD3
    ..
    (All above are set to Don't Process)

    ClearCal
    Calibrate STD*
    Quantitate SMP*
    Summarize
    (All above are set to Normal)

    I'd use linear through zero (all your STD have the same concentration anyway).

    I'd ask though, why so many standards? RSD problems? Stability problems?
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